Part 3: Algorithm Analysis Similarly to what we have done in class and in the labs, for each of
the problems below (1-4) provide the following: i. a description of the worst-cose input, i.e., the
input that would cause the largest running time ii. a summation used to calculate number of steps
for the worst case iii. an explanation of how the code relates to the summation a. (loops require
discussing number of iterations and steps per iteration) b. (recursion requires a diagram) iv. a
bound, that, is a function g(n) such that R(n)(g(n)) where n is a suitable measure of the problem
size and R(n) is the running time of the algorithm. Your summation should not be overly
pessimistic, even when it leads to the same g(n) as another summation. For example, for the code
for ( int i=0;i) tot = tot +a[j]; \}.
Part 3 Algorithm Analysis Similarly to what we have done in class an.pdf
1. Part 3: Algorithm Analysis Similarly to what we have done in class and in the labs, for each of
the problems below (1-4) provide the following: i. a description of the worst-cose input, i.e., the
input that would cause the largest running time ii. a summation used to calculate number of steps
for the worst case iii. an explanation of how the code relates to the summation a. (loops require
discussing number of iterations and steps per iteration) b. (recursion requires a diagram) iv. a
bound, that, is a function g(n) such that R(n)(g(n)) where n is a suitable measure of the problem
size and R(n) is the running time of the algorithm. Your summation should not be overly
pessimistic, even when it leads to the same g(n) as another summation. For example, for the code
for ( int i=0;i) tot = tot +a[j]; }
![Part 3: Algorithm Analysis Similarly to what we have done in class and in the labs, for each of
the problems below (1-4) provide the following: i. a description of the worst-cose input, i.e., the
input that would cause the largest running time ii. a summation used to calculate number of steps
for the worst case iii. an explanation of how the code relates to the summation a. (loops require
discussing number of iterations and steps per iteration) b. (recursion requires a diagram) iv. a
bound, that, is a function g(n) such that R(n)(g(n)) where n is a suitable measure of the problem
size and R(n) is the running time of the algorithm. Your summation should not be overly
pessimistic, even when it leads to the same g(n) as another summation. For example, for the code
for ( int i=0;i) tot = tot +a[j]; }](https://image.slidesharecdn.com/part3algorithmanalysissimilarlytowhatwehavedoneinclassan-230402033006-38f9add3/85/Part-3-Algorithm-Analysis-Similarly-to-what-we-have-done-in-class-an-pdf-1-320.jpg)
